{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/tutorials/2-Advance/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXaoZs2lh1hi"
   },
   "source": [
    "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading Using Ensemble Strategy\n",
    "\n",
    "Tutorials to use OpenAI DRL to trade multiple stocks using ensemble strategy in one Jupyter Notebook | Presented at ICAIF 2020\n",
    "\n",
    "* This notebook is the reimplementation of our paper: Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, using FinRL.\n",
    "* Check out medium blog for detailed explanations: https://medium.com/@ai4finance/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02\n",
    "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
    "* **Pytorch Version** \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lGunVt8oLCVS"
   },
   "source": [
    "# Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HOzAKQ-SLGX6"
   },
   "source": [
    "* [1. Problem Definition](#0)\n",
    "* [2. Getting Started - Load Python packages](#1)\n",
    "    * [2.1. Install Packages](#1.1)    \n",
    "    * [2.2. Check Additional Packages](#1.2)\n",
    "    * [2.3. Import Packages](#1.3)\n",
    "    * [2.4. Create Folders](#1.4)\n",
    "* [3. Download Data](#2)\n",
    "* [4. Preprocess Data](#3)        \n",
    "    * [4.1. Technical Indicators](#3.1)\n",
    "    * [4.2. Perform Feature Engineering](#3.2)\n",
    "* [5.Build Environment](#4)  \n",
    "    * [5.1. Training & Trade Data Split](#4.1)\n",
    "    * [5.2. User-defined Environment](#4.2)   \n",
    "    * [5.3. Initialize Environment](#4.3)    \n",
    "* [6.Implement DRL Algorithms](#5)  \n",
    "* [7.Backtesting Performance](#6)  \n",
    "    * [7.1. BackTestStats](#6.1)\n",
    "    * [7.2. BackTestPlot](#6.2)   \n",
    "    * [7.3. Baseline Stats](#6.3)   \n",
    "    * [7.3. Compare to Stock Market Index](#6.4)             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sApkDlD9LIZv"
   },
   "source": [
    "<a id='0'></a>\n",
    "# Part 1. Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HjLD2TZSLKZ-"
   },
   "source": [
    "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
    "\n",
    "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
    "\n",
    "\n",
    "* Action: The action space describes the allowed actions that the agent interacts with the\n",
    "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
    "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
    "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
    "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
    "\n",
    "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
    "values at state s′ and s, respectively\n",
    "\n",
    "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
    "our trading agent observes many different features to better learn in an interactive environment.\n",
    "\n",
    "* Environment: Dow 30 consituents\n",
    "\n",
    "\n",
    "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ffsre789LY08"
   },
   "source": [
    "<a id='1'></a>\n",
    "# Part 2. Getting Started- Load Python Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uy5_PTmOh1hj"
   },
   "source": [
    "<a id='1.1'></a>\n",
    "## 2.1. Install all the packages through FinRL library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "mPT0ipYE28wL",
    "outputId": "5002eb1d-6fec-4458-a873-f5f6b6f57e5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-g3dq0x8o\n",
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      "  Running command git clone -q https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-shut_jzl/elegantrl_250c287a6f2d4ea1812e2edf445cf096\n",
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      "  Stored in directory: /root/.cache/pip/wheels/87/67/2e/6147e7912fe37f5408b80d07527dab807c1d25f5c403a9538a\n",
      "Successfully built finrl elegantrl pyfolio empyrical exchange-calendars gputil thriftpy2 gpustat gym AutoROM.accept-rom-license\n",
      "Installing collected packages: setuptools, requests, multidict, frozenlist, yarl, platformdirs, lxml, distlib, asynctest, async-timeout, aiosignal, virtualenv, PyYAML, pycares, ply, opencensus-context, gym, grpcio, blessed, AutoROM.accept-rom-license, autorom, aiohttp, websockets, websocket-client, toml, thriftpy2, tensorboardX, stable-baselines3, ray, pymysql, pyluach, pybullet, py-spy, psycopg2-binary, prometheus-client, opencensus, nodeenv, mock, identify, gpustat, empyrical, deprecation, cryptography, colorful, cfgv, box2d-py, ale-py, aiohttp-cors, aiodns, yfinance, wrds, stockstats, pyfolio, pre-commit, lz4, jqdatasdk, gputil, exchange-calendars, elegantrl, ccxt, alpaca-trade-api, finrl\n",
      "  Attempting uninstall: setuptools\n",
      "    Found existing installation: setuptools 57.4.0\n",
      "    Uninstalling setuptools-57.4.0:\n",
      "      Successfully uninstalled setuptools-57.4.0\n",
      "  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.23.0\n",
      "    Uninstalling requests-2.23.0:\n",
      "      Successfully uninstalled requests-2.23.0\n",
      "  Attempting uninstall: lxml\n",
      "    Found existing installation: lxml 4.2.6\n",
      "    Uninstalling lxml-4.2.6:\n",
      "      Successfully uninstalled lxml-4.2.6\n",
      "  Attempting uninstall: PyYAML\n",
      "    Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
      "      Successfully uninstalled PyYAML-3.13\n",
      "  Attempting uninstall: gym\n",
      "    Found existing installation: gym 0.17.3\n",
      "    Uninstalling gym-0.17.3:\n",
      "      Successfully uninstalled gym-0.17.3\n",
      "  Attempting uninstall: grpcio\n",
      "    Found existing installation: grpcio 1.46.3\n",
      "    Uninstalling grpcio-1.46.3:\n",
      "      Successfully uninstalled grpcio-1.46.3\n",
      "  Attempting uninstall: prometheus-client\n",
      "    Found existing installation: prometheus-client 0.14.1\n",
      "    Uninstalling prometheus-client-0.14.1:\n",
      "      Successfully uninstalled prometheus-client-0.14.1\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
      "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
      "Successfully installed AutoROM.accept-rom-license-0.4.2 PyYAML-6.0 aiodns-3.0.0 aiohttp-3.8.1 aiohttp-cors-0.7.0 aiosignal-1.2.0 ale-py-0.7.5 alpaca-trade-api-2.1.0 async-timeout-4.0.2 asynctest-0.13.0 autorom-0.4.2 blessed-1.19.1 box2d-py-2.3.8 ccxt-1.66.32 cfgv-3.3.1 colorful-0.5.4 cryptography-37.0.2 deprecation-2.1.0 distlib-0.3.4 elegantrl-0.3.3 empyrical-0.5.5 exchange-calendars-3.6.3 finrl-0.3.5 frozenlist-1.3.0 gpustat-1.0.0b1 gputil-1.4.0 grpcio-1.43.0 gym-0.21.0 identify-2.5.1 jqdatasdk-1.8.10 lxml-4.9.0 lz4-4.0.1 mock-4.0.3 multidict-6.0.2 nodeenv-1.6.0 opencensus-0.9.0 opencensus-context-0.1.2 platformdirs-2.5.2 ply-3.11 pre-commit-2.19.0 prometheus-client-0.13.1 psycopg2-binary-2.9.3 py-spy-0.3.12 pybullet-3.2.5 pycares-4.1.2 pyfolio-0.9.2+75.g4b901f6 pyluach-2.0.0 pymysql-1.0.2 ray-1.12.1 requests-2.27.1 setuptools-59.5.0 stable-baselines3-1.5.0 stockstats-0.4.1 tensorboardX-2.5 thriftpy2-0.4.14 toml-0.10.2 virtualenv-20.14.1 websocket-client-1.3.2 websockets-10.3 wrds-3.1.1 yarl-1.7.2 yfinance-0.1.70\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "pkg_resources"
        ]
       }
      }
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# ## install finrl library\n",
    "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osBHhVysOEzi"
   },
   "source": [
    "\n",
    "<a id='1.2'></a>\n",
    "## 2.2. Check if the additional packages needed are present, if not install them. \n",
    "* Yahoo Finance API\n",
    "* pandas\n",
    "* numpy\n",
    "* matplotlib\n",
    "* stockstats\n",
    "* OpenAI gym\n",
    "* stable-baselines\n",
    "* tensorflow\n",
    "* pyfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nGv01K8Sh1hn"
   },
   "source": [
    "<a id='1.3'></a>\n",
    "## 2.3. Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "EeMK7Uentj1V"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "lPqeTTwoh1hn"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib.use('Agg')\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.agents.stablebaselines3.models import DRLAgent,DRLEnsembleAgent\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../FinRL-Library\")\n",
    "\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2owTj985RW4"
   },
   "source": [
    "<a id='1.4'></a>\n",
    "## 2.4. Create Folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "w9A8CN5R5PuZ"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
    "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
    "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
    "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
    "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
    "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A289rQWMh1hq"
   },
   "source": [
    "<a id='2'></a>\n",
    "# Part 3. Download Data\n",
    "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
    "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
    "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NPeQ7iS-LoMm"
   },
   "source": [
    "\n",
    "\n",
    "-----\n",
    "class YahooDownloader:\n",
    "    Provides methods for retrieving daily stock data from\n",
    "    Yahoo Finance API\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "        start_date : str\n",
    "            start date of the data (modified from config.py)\n",
    "        end_date : str\n",
    "            end date of the data (modified from config.py)\n",
    "        ticker_list : list\n",
    "            a list of stock tickers (modified from config.py)\n",
    "\n",
    "    Methods\n",
    "    -------\n",
    "    fetch_data()\n",
    "        Fetches data from yahoo API\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JzqRRTOX6aFu",
    "outputId": "1234fde6-bb5a-4e64-b975-056fc3c0c95a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n"
     ]
    }
   ],
   "source": [
    "print(config_tickers.DOW_30_TICKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yCKm4om-s9kE",
    "outputId": "83273b04-ff5c-457c-e3ed-ded275cb11a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (96942, 8)\n"
     ]
    }
   ],
   "source": [
    "df = YahooDownloader(start_date = '2009-04-01',\n",
    "                     end_date = '2022-06-01',\n",
    "                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "GiRuFOTOtj1Y",
    "outputId": "3ab9aca2-f0d9-4dac-a2da-a5cbafbf67d4"
   },
   "outputs": [
    {
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       "         date       open       high        low      close       volume   tic  \\\n",
       "0  2009-04-01   3.717500   3.892857   3.710357   3.318997  589372000.0  AAPL   \n",
       "1  2009-04-01  48.779999  48.930000  47.099998  36.759624   10850100.0  AMGN   \n",
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       "      <th>low</th>\n",
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       "      <th>day</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96937</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>503.619995</td>\n",
       "      <td>504.109985</td>\n",
       "      <td>495.660004</td>\n",
       "      <td>496.779999</td>\n",
       "      <td>4003100.0</td>\n",
       "      <td>UNH</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>96938</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>210.380005</td>\n",
       "      <td>214.350006</td>\n",
       "      <td>209.110001</td>\n",
       "      <td>212.169998</td>\n",
       "      <td>9586400.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>96939</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>51.259998</td>\n",
       "      <td>51.560001</td>\n",
       "      <td>50.849998</td>\n",
       "      <td>51.290001</td>\n",
       "      <td>25016600.0</td>\n",
       "      <td>VZ</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>96940</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>43.480000</td>\n",
       "      <td>44.270000</td>\n",
       "      <td>43.049999</td>\n",
       "      <td>43.830002</td>\n",
       "      <td>8192000.0</td>\n",
       "      <td>WBA</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>96941</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>127.459999</td>\n",
       "      <td>129.899994</td>\n",
       "      <td>127.419998</td>\n",
       "      <td>128.630005</td>\n",
       "      <td>12304100.0</td>\n",
       "      <td>WMT</td>\n",
       "      <td>1</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  "
      ],
      "text/plain": [
       "             date        open        high         low       close      volume  \\\n",
       "96937  2022-05-31  503.619995  504.109985  495.660004  496.779999   4003100.0   \n",
       "96938  2022-05-31  210.380005  214.350006  209.110001  212.169998   9586400.0   \n",
       "96939  2022-05-31   51.259998   51.560001   50.849998   51.290001  25016600.0   \n",
       "96940  2022-05-31   43.480000   44.270000   43.049999   43.830002   8192000.0   \n",
       "96941  2022-05-31  127.459999  129.899994  127.419998  128.630005  12304100.0   \n",
       "\n",
       "       tic  day  \n",
       "96937  UNH    1  \n",
       "96938    V    1  \n",
       "96939   VZ    1  \n",
       "96940  WBA    1  \n",
       "96941  WMT    1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96942, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    "id": "4hYkeaPiICHS",
    "outputId": "06337afc-3e17-488b-8941-cc6236f291cd"
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       "         date       open       high        low      close       volume   tic  \\\n",
       "0  2009-04-01   3.717500   3.892857   3.710357   3.318997  589372000.0  AAPL   \n",
       "1  2009-04-01  48.779999  48.930000  47.099998  36.759624   10850100.0  AMGN   \n",
       "2  2009-04-01  13.340000  14.640000  13.080000  11.861942   27701800.0   AXP   \n",
       "3  2009-04-01  34.520000  35.599998  34.209999  26.850744    9288800.0    BA   \n",
       "4  2009-04-01  27.500000  29.520000  27.440001  20.091990   15308300.0   CAT   \n",
       "\n",
       "   day  \n",
       "0    2  \n",
       "1    2  \n",
       "2    2  \n",
       "3    2  \n",
       "4    2  "
      ]
     },
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     "metadata": {},
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    }
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   "source": [
    "df.sort_values(['date','tic']).head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
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    "id": "a2vryMsdNL9H",
    "outputId": "6032493a-ebe8-4f72-ab27-a7f4b5a650c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.tic.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "XcNyXa7RNPrF",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    3315\n",
       "AMGN    3315\n",
       "WMT     3315\n",
       "WBA     3315\n",
       "VZ      3315\n",
       "V       3315\n",
       "UNH     3315\n",
       "TRV     3315\n",
       "PG      3315\n",
       "NKE     3315\n",
       "MSFT    3315\n",
       "MRK     3315\n",
       "MMM     3315\n",
       "MCD     3315\n",
       "KO      3315\n",
       "JPM     3315\n",
       "JNJ     3315\n",
       "INTC    3315\n",
       "IBM     3315\n",
       "HON     3315\n",
       "HD      3315\n",
       "GS      3315\n",
       "DIS     3315\n",
       "CVX     3315\n",
       "CSCO    3315\n",
       "CRM     3315\n",
       "CAT     3315\n",
       "BA      3315\n",
       "AXP     3315\n",
       "DOW      807\n",
       "Name: tic, dtype: int64"
      ]
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  {
   "cell_type": "markdown",
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    "id": "uqC6c40Zh1iH"
   },
   "source": [
    "# Part 4: Preprocess Data\n",
    "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
    "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
    "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "kM5bH9uroCeg"
   },
   "outputs": [],
   "source": [
    "tech_indicators = ['macd',\n",
    " 'rsi_30',\n",
    " 'cci_30',\n",
    " 'dx_30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "jgXfBcjxtj1a",
    "outputId": "154d8810-e754-448b-b340-2f3f63996c43",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n"
     ]
    }
   ],
   "source": [
    "fe = FeatureEngineer(\n",
    "                    use_technical_indicator=True,\n",
    "                    tech_indicator_list = tech_indicators,\n",
    "                    use_turbulence=True,\n",
    "                    user_defined_feature = False)\n",
    "\n",
    "processed = fe.preprocess_data(df)\n",
    "processed = processed.copy()\n",
    "processed = processed.fillna(0)\n",
    "processed = processed.replace(np.inf,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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    },
    "id": "grvhGJJII3Xn",
    "outputId": "467b45da-4b42-4a38-88a2-b77eb07afde2"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>day</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>turbulence</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31195</th>\n",
       "      <td>2013-07-10</td>\n",
       "      <td>34.340000</td>\n",
       "      <td>34.810001</td>\n",
       "      <td>34.320000</td>\n",
       "      <td>29.195557</td>\n",
       "      <td>29658800.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>2</td>\n",
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       "      <td>25.147764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38288</th>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>85.440002</td>\n",
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       "    <tr>\n",
       "      <th>13493</th>\n",
       "      <td>2011-02-03</td>\n",
       "      <td>40.400002</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>40.279999</td>\n",
       "      <td>35.662556</td>\n",
       "      <td>9513800.0</td>\n",
       "      <td>DIS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.459549</td>\n",
       "      <td>64.036384</td>\n",
       "      <td>135.758554</td>\n",
       "      <td>30.713894</td>\n",
       "      <td>23.942581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21567</th>\n",
       "      <td>2012-03-13</td>\n",
       "      <td>32.240002</td>\n",
       "      <td>32.689999</td>\n",
       "      <td>32.150002</td>\n",
       "      <td>26.500782</td>\n",
       "      <td>48951700.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1</td>\n",
       "      <td>0.550023</td>\n",
       "      <td>69.088606</td>\n",
       "      <td>126.129096</td>\n",
       "      <td>40.349050</td>\n",
       "      <td>31.261426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57504</th>\n",
       "      <td>2017-02-14</td>\n",
       "      <td>48.310001</td>\n",
       "      <td>48.470001</td>\n",
       "      <td>47.990002</td>\n",
       "      <td>38.127930</td>\n",
       "      <td>20847200.0</td>\n",
       "      <td>VZ</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.691687</td>\n",
       "      <td>41.270201</td>\n",
       "      <td>-81.118392</td>\n",
       "      <td>38.906507</td>\n",
       "      <td>10.815254</td>\n",
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      "text/plain": [
       "             date       open       high        low      close      volume  \\\n",
       "31195  2013-07-10  34.340000  34.810001  34.320000  29.195557  29658800.0   \n",
       "38288  2014-06-30  85.440002  86.070000  85.070000  78.907906   6991400.0   \n",
       "13493  2011-02-03  40.400002  40.750000  40.279999  35.662556   9513800.0   \n",
       "21567  2012-03-13  32.240002  32.689999  32.150002  26.500782  48951700.0   \n",
       "57504  2017-02-14  48.310001  48.470001  47.990002  38.127930  20847200.0   \n",
       "\n",
       "        tic  day      macd     rsi_30      cci_30      dx_30  turbulence  \n",
       "31195  MSFT    2  0.058458  57.570755    3.823830   3.633704   25.147764  \n",
       "38288   DIS    0  0.530425  60.739557  162.216666  27.900607    8.387872  \n",
       "13493   DIS    3  0.459549  64.036384  135.758554  30.713894   23.942581  \n",
       "21567  MSFT    1  0.550023  69.088606  126.129096  40.349050   31.261426  \n",
       "57504    VZ    1 -0.691687  41.270201  -81.118392  38.906507   10.815254  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-QsYaY0Dh1iw"
   },
   "source": [
    "<a id='4'></a>\n",
    "# Part 5. Design Environment\n",
    "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
    "\n",
    "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
    "\n",
    "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q2zqII8rMIqn",
    "outputId": "6f958c23-155e-4bb0-f91d-8627d176fa3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock Dimension: 29, State Space: 175\n"
     ]
    }
   ],
   "source": [
    "stock_dimension = len(processed.tic.unique())\n",
    "state_space = 1 + 2*stock_dimension + len(tech_indicators)*stock_dimension\n",
    "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "AWyp84Ltto19"
   },
   "outputs": [],
   "source": [
    "env_kwargs = {\n",
    "    \"hmax\": 100, \n",
    "    \"initial_amount\": 1000000, \n",
    "    \"buy_cost_pct\": 0.001, \n",
    "    \"sell_cost_pct\": 0.001, \n",
    "    \"state_space\": state_space, \n",
    "    \"stock_dim\": stock_dimension, \n",
    "    \"tech_indicator_list\": tech_indicators,\n",
    "    \"action_space\": stock_dimension, \n",
    "    \"reward_scaling\": 1e-4,\n",
    "    \"print_verbosity\":5\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMNR5nHjh1iz"
   },
   "source": [
    "<a id='5'></a>\n",
    "# Part 6: Implement DRL Algorithms\n",
    "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
    "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
    "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
    "design their own DRL algorithms by adapting these DRL algorithms.\n",
    "\n",
    "* In this notebook, we are training and validating 3 agents (A2C, PPO, DDPG) using Rolling-window Ensemble Method ([reference code](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020/blob/80415db8fa7b2179df6bd7e81ce4fe8dbf913806/model/models.py#L92))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "v-gthCxMtj1d"
   },
   "outputs": [],
   "source": [
    "rebalance_window = 63 # rebalance_window is the number of days to retrain the model\n",
    "validation_window = 63 # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)\n",
    "train_start = '2009-04-01'\n",
    "train_end = '2020-04-01'\n",
    "val_test_start = '2020-04-01'\n",
    "val_test_end = '2022-06-01'\n",
    "\n",
    "ensemble_agent = DRLEnsembleAgent(df=processed,\n",
    "                 train_period=(train_start,train_end),\n",
    "                 val_test_period=(val_test_start,val_test_end),\n",
    "                 rebalance_window=rebalance_window, \n",
    "                 validation_window=validation_window, \n",
    "                 **env_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "KsfEHa_Etj1d",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "A2C_model_kwargs = {\n",
    "                    'n_steps': 5,\n",
    "                    'ent_coef': 0.01,\n",
    "                    'learning_rate': 0.0005\n",
    "                    }\n",
    "\n",
    "PPO_model_kwargs = {\n",
    "                    \"ent_coef\":0.01,\n",
    "                    \"n_steps\": 2048,\n",
    "                    \"learning_rate\": 0.00025,\n",
    "                    \"batch_size\": 64\n",
    "                    }\n",
    "\n",
    "DDPG_model_kwargs = {\n",
    "                      #\"action_noise\":\"ornstein_uhlenbeck\",\n",
    "                      \"buffer_size\": 10_000,\n",
    "                      \"learning_rate\": 0.0005,\n",
    "                      \"batch_size\": 64\n",
    "                    }\n",
    "\n",
    "timesteps_dict = {'a2c' : 10_000, \n",
    "                 'ppo' : 10_000, \n",
    "                 'ddpg' : 10_000\n",
    "                 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_1lyCECstj1e",
    "outputId": "634f65a8-abf7-43fc-ef7f-b15799746448",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============Start Ensemble Strategy============\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2020-04-02\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_126_1\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 51          |\n",
      "|    iterations         | 100         |\n",
      "|    time_elapsed       | 9           |\n",
      "|    total_timesteps    | 500         |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | -0.0443     |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 99          |\n",
      "|    policy_loss        | -79.5       |\n",
      "|    reward             | -0.12137867 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 6.7         |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 69        |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 14        |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.00629  |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | -30.2     |\n",
      "|    reward             | 2.1963325 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 0.979     |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 77        |\n",
      "|    iterations         | 300       |\n",
      "|    time_elapsed       | 19        |\n",
      "|    total_timesteps    | 1500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.1     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 299       |\n",
      "|    policy_loss        | -276      |\n",
      "|    reward             | 3.3443768 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 60.3      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 83        |\n",
      "|    iterations         | 400       |\n",
      "|    time_elapsed       | 24        |\n",
      "|    total_timesteps    | 2000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.0244   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 399       |\n",
      "|    policy_loss        | 69.3      |\n",
      "|    reward             | 0.6587393 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 6.87      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 86        |\n",
      "|    iterations         | 500       |\n",
      "|    time_elapsed       | 28        |\n",
      "|    total_timesteps    | 2500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0.0231    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 499       |\n",
      "|    policy_loss        | 259       |\n",
      "|    reward             | 1.7264832 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 50        |\n",
      "-------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 89          |\n",
      "|    iterations         | 600         |\n",
      "|    time_elapsed       | 33          |\n",
      "|    total_timesteps    | 3000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | -0.427      |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 599         |\n",
      "|    policy_loss        | -17.3       |\n",
      "|    reward             | -0.37551424 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 0.593       |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 91         |\n",
      "|    iterations         | 700        |\n",
      "|    time_elapsed       | 38         |\n",
      "|    total_timesteps    | 3500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 699        |\n",
      "|    policy_loss        | -27.7      |\n",
      "|    reward             | 0.74899477 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 0.581      |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 93        |\n",
      "|    iterations         | 800       |\n",
      "|    time_elapsed       | 42        |\n",
      "|    total_timesteps    | 4000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 799       |\n",
      "|    policy_loss        | 126       |\n",
      "|    reward             | 0.8671263 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 13.5      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 94        |\n",
      "|    iterations         | 900       |\n",
      "|    time_elapsed       | 47        |\n",
      "|    total_timesteps    | 4500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.13     |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 899       |\n",
      "|    policy_loss        | -281      |\n",
      "|    reward             | 2.0638134 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 48.1      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 95         |\n",
      "|    iterations         | 1000       |\n",
      "|    time_elapsed       | 52         |\n",
      "|    total_timesteps    | 5000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 5.96e-08   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 999        |\n",
      "|    policy_loss        | -21.3      |\n",
      "|    reward             | -13.721387 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 6.8        |\n",
      "--------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 96       |\n",
      "|    iterations         | 1100     |\n",
      "|    time_elapsed       | 57       |\n",
      "|    total_timesteps    | 5500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.2    |\n",
      "|    explained_variance | 0        |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 1099     |\n",
      "|    policy_loss        | -14.8    |\n",
      "|    reward             | 3.635222 |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 6.59     |\n",
      "------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 1200       |\n",
      "|    time_elapsed       | 61         |\n",
      "|    total_timesteps    | 6000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | -1.19e-07  |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1199       |\n",
      "|    policy_loss        | -108       |\n",
      "|    reward             | 0.58974755 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 7.6        |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 97        |\n",
      "|    iterations         | 1300      |\n",
      "|    time_elapsed       | 66        |\n",
      "|    total_timesteps    | 6500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0.0492    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1299      |\n",
      "|    policy_loss        | -105      |\n",
      "|    reward             | 1.4849552 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 8.46      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 98        |\n",
      "|    iterations         | 1400      |\n",
      "|    time_elapsed       | 71        |\n",
      "|    total_timesteps    | 7000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -1.19e-07 |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1399      |\n",
      "|    policy_loss        | -21       |\n",
      "|    reward             | 1.7943225 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 8.06      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 98         |\n",
      "|    iterations         | 1500       |\n",
      "|    time_elapsed       | 75         |\n",
      "|    total_timesteps    | 7500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1499       |\n",
      "|    policy_loss        | 166        |\n",
      "|    reward             | -1.5894299 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 22.2       |\n",
      "--------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 99       |\n",
      "|    iterations         | 1600     |\n",
      "|    time_elapsed       | 80       |\n",
      "|    total_timesteps    | 8000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.3    |\n",
      "|    explained_variance | -0.0581  |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 1599     |\n",
      "|    policy_loss        | 586      |\n",
      "|    reward             | 2.051974 |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 323      |\n",
      "------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 99          |\n",
      "|    iterations         | 1700        |\n",
      "|    time_elapsed       | 85          |\n",
      "|    total_timesteps    | 8500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | -0.0844     |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1699        |\n",
      "|    policy_loss        | 44.9        |\n",
      "|    reward             | -0.46527642 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 2.43        |\n",
      "---------------------------------------\n",
      "----------------------------------------\n",
      "| time/                 |              |\n",
      "|    fps                | 100          |\n",
      "|    iterations         | 1800         |\n",
      "|    time_elapsed       | 89           |\n",
      "|    total_timesteps    | 9000         |\n",
      "| train/                |              |\n",
      "|    entropy_loss       | -41.3        |\n",
      "|    explained_variance | 0            |\n",
      "|    learning_rate      | 0.0005       |\n",
      "|    n_updates          | 1799         |\n",
      "|    policy_loss        | -102         |\n",
      "|    reward             | -0.005320606 |\n",
      "|    std                | 1.01         |\n",
      "|    value_loss         | 17.7         |\n",
      "----------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1900      |\n",
      "|    time_elapsed       | 94        |\n",
      "|    total_timesteps    | 9500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1899      |\n",
      "|    policy_loss        | -183      |\n",
      "|    reward             | 1.6272634 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 24.2      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 2000      |\n",
      "|    time_elapsed       | 99        |\n",
      "|    total_timesteps    | 10000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0.00397   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1999      |\n",
      "|    policy_loss        | -218      |\n",
      "|    reward             | 5.7600756 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 37.8      |\n",
      "-------------------------------------\n",
      "======A2C Validation from:  2020-04-02 to  2020-07-02\n",
      "A2C Sharpe Ratio:  0.22293886672921612\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_126_1\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    fps             | 118       |\n",
      "|    iterations      | 1         |\n",
      "|    time_elapsed    | 17        |\n",
      "|    total_timesteps | 2048      |\n",
      "| train/             |           |\n",
      "|    reward          | 1.0927399 |\n",
      "----------------------------------\n",
      "day: 2769, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 2894172.32\n",
      "total_reward: 1894172.32\n",
      "total_cost: 333876.42\n",
      "total_trades: 77330\n",
      "Sharpe: 0.666\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 110         |\n",
      "|    iterations           | 2           |\n",
      "|    time_elapsed         | 37          |\n",
      "|    total_timesteps      | 4096        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022042647 |\n",
      "|    clip_fraction        | 0.251       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | -0.0165     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 4.92        |\n",
      "|    n_updates            | 10          |\n",
      "|    policy_gradient_loss | -0.0269     |\n",
      "|    reward               | 0.35970497  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 10.3        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 109         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 55          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.018107109 |\n",
      "|    clip_fraction        | 0.228       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | 0.00171     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 11.3        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0292     |\n",
      "|    reward               | 3.183098    |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 46.3        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 109        |\n",
      "|    iterations           | 4          |\n",
      "|    time_elapsed         | 74         |\n",
      "|    total_timesteps      | 8192       |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02354681 |\n",
      "|    clip_fraction        | 0.221      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -41.3      |\n",
      "|    explained_variance   | -0.0015    |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 26.9       |\n",
      "|    n_updates            | 30         |\n",
      "|    policy_gradient_loss | -0.0242    |\n",
      "|    reward               | -0.4996353 |\n",
      "|    std                  | 1.01       |\n",
      "|    value_loss           | 59.6       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 109         |\n",
      "|    iterations           | 5           |\n",
      "|    time_elapsed         | 93          |\n",
      "|    total_timesteps      | 10240       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.023231182 |\n",
      "|    clip_fraction        | 0.253       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | 0.0179      |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 12          |\n",
      "|    n_updates            | 40          |\n",
      "|    policy_gradient_loss | -0.0203     |\n",
      "|    reward               | 0.15895143  |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 23.9        |\n",
      "-----------------------------------------\n",
      "======PPO Validation from:  2020-04-02 to  2020-07-02\n",
      "PPO Sharpe Ratio:  0.15651256586206605\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_126_1\n",
      "day: 2769, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 2576580.36\n",
      "total_reward: 1576580.36\n",
      "total_cost: 1278.39\n",
      "total_trades: 33200\n",
      "Sharpe: 0.579\n",
      "=================================\n",
      "-----------------------------------\n",
      "| time/              |            |\n",
      "|    episodes        | 4          |\n",
      "|    fps             | 73         |\n",
      "|    time_elapsed    | 151        |\n",
      "|    total_timesteps | 11080      |\n",
      "| train/             |            |\n",
      "|    actor_loss      | -8.58      |\n",
      "|    critic_loss     | 304        |\n",
      "|    learning_rate   | 0.0005     |\n",
      "|    n_updates       | 8310       |\n",
      "|    reward          | -10.288755 |\n",
      "-----------------------------------\n",
      "======DDPG Validation from:  2020-04-02 to  2020-07-02\n",
      "======Best Model Retraining from:  2009-04-01 to  2020-07-02\n",
      "======Trading from:  2020-07-02 to  2020-10-01\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2020-07-02\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_189_1\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 100        |\n",
      "|    time_elapsed       | 4          |\n",
      "|    total_timesteps    | 500        |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | -0.875     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 99         |\n",
      "|    policy_loss        | -38.3      |\n",
      "|    reward             | 0.28885353 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 5.71       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 105       |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 9         |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.235    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | 27.1      |\n",
      "|    reward             | 1.3381864 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 1.65      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 105      |\n",
      "|    iterations         | 300      |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 1500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.2    |\n",
      "|    explained_variance | 0.16     |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 299      |\n",
      "|    policy_loss        | -113     |\n",
      "|    reward             | 1.921464 |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 15.3     |\n",
      "------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 400        |\n",
      "|    time_elapsed       | 19         |\n",
      "|    total_timesteps    | 2000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | -1.19e-07  |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 399        |\n",
      "|    policy_loss        | -62.3      |\n",
      "|    reward             | -1.5135788 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 7.76       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 105         |\n",
      "|    iterations         | 500         |\n",
      "|    time_elapsed       | 23          |\n",
      "|    total_timesteps    | 2500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 499         |\n",
      "|    policy_loss        | 67.5        |\n",
      "|    reward             | -0.29005492 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 3.63        |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 600        |\n",
      "|    time_elapsed       | 28         |\n",
      "|    total_timesteps    | 3000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0.307      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 599        |\n",
      "|    policy_loss        | -58.7      |\n",
      "|    reward             | -1.7230979 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 1.99       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 700        |\n",
      "|    time_elapsed       | 33         |\n",
      "|    total_timesteps    | 3500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0.00331    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 699        |\n",
      "|    policy_loss        | 4.16       |\n",
      "|    reward             | -1.7484325 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 1.61       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 105         |\n",
      "|    iterations         | 800         |\n",
      "|    time_elapsed       | 37          |\n",
      "|    total_timesteps    | 4000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 1.19e-07    |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 799         |\n",
      "|    policy_loss        | 116         |\n",
      "|    reward             | -0.07568552 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 7.78        |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 900        |\n",
      "|    time_elapsed       | 42         |\n",
      "|    total_timesteps    | 4500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 899        |\n",
      "|    policy_loss        | 65.2       |\n",
      "|    reward             | -1.4678386 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 3.51       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 1000       |\n",
      "|    time_elapsed       | 47         |\n",
      "|    total_timesteps    | 5000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.000785   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 999        |\n",
      "|    policy_loss        | -83.8      |\n",
      "|    reward             | 0.62492967 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 5.58       |\n",
      "--------------------------------------\n",
      "----------------------------------------\n",
      "| time/                 |              |\n",
      "|    fps                | 105          |\n",
      "|    iterations         | 1100         |\n",
      "|    time_elapsed       | 52           |\n",
      "|    total_timesteps    | 5500         |\n",
      "| train/                |              |\n",
      "|    entropy_loss       | -41.3        |\n",
      "|    explained_variance | 0.273        |\n",
      "|    learning_rate      | 0.0005       |\n",
      "|    n_updates          | 1099         |\n",
      "|    policy_loss        | -20.6        |\n",
      "|    reward             | -0.114883795 |\n",
      "|    std                | 1            |\n",
      "|    value_loss         | 0.544        |\n",
      "----------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 105         |\n",
      "|    iterations         | 1200        |\n",
      "|    time_elapsed       | 56          |\n",
      "|    total_timesteps    | 6000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 0.0605      |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1199        |\n",
      "|    policy_loss        | -25.5       |\n",
      "|    reward             | -0.02178487 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 1           |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 1300       |\n",
      "|    time_elapsed       | 61         |\n",
      "|    total_timesteps    | 6500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.0702     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1299       |\n",
      "|    policy_loss        | 32.6       |\n",
      "|    reward             | -1.1368469 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.977      |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 105         |\n",
      "|    iterations         | 1400        |\n",
      "|    time_elapsed       | 66          |\n",
      "|    total_timesteps    | 7000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 0.273       |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1399        |\n",
      "|    policy_loss        | 20.6        |\n",
      "|    reward             | -0.31444284 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 0.284       |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 1500       |\n",
      "|    time_elapsed       | 71         |\n",
      "|    total_timesteps    | 7500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | -1.19e-07  |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1499       |\n",
      "|    policy_loss        | -9.8       |\n",
      "|    reward             | 0.78083295 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 1.67       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 105       |\n",
      "|    iterations         | 1600      |\n",
      "|    time_elapsed       | 75        |\n",
      "|    total_timesteps    | 8000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1599      |\n",
      "|    policy_loss        | 119       |\n",
      "|    reward             | 3.2594426 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 11.6      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 1700       |\n",
      "|    time_elapsed       | 80         |\n",
      "|    total_timesteps    | 8500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | -0.0022    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1699       |\n",
      "|    policy_loss        | -497       |\n",
      "|    reward             | -5.7647114 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 166        |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 105         |\n",
      "|    iterations         | 1800        |\n",
      "|    time_elapsed       | 85          |\n",
      "|    total_timesteps    | 9000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.4       |\n",
      "|    explained_variance | -0.117      |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1799        |\n",
      "|    policy_loss        | 59.5        |\n",
      "|    reward             | -0.27958715 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 2.98        |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 105        |\n",
      "|    iterations         | 1900       |\n",
      "|    time_elapsed       | 90         |\n",
      "|    total_timesteps    | 9500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | -1.19e-07  |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1899       |\n",
      "|    policy_loss        | -27.9      |\n",
      "|    reward             | 0.19883963 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.761      |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 104        |\n",
      "|    iterations         | 2000       |\n",
      "|    time_elapsed       | 95         |\n",
      "|    total_timesteps    | 10000      |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1999       |\n",
      "|    policy_loss        | -126       |\n",
      "|    reward             | -1.7379556 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 19.4       |\n",
      "--------------------------------------\n",
      "======A2C Validation from:  2020-07-02 to  2020-10-01\n",
      "A2C Sharpe Ratio:  0.115657649490576\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_189_1\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    fps             | 115      |\n",
      "|    iterations      | 1        |\n",
      "|    time_elapsed    | 17       |\n",
      "|    total_timesteps | 2048     |\n",
      "| train/             |          |\n",
      "|    reward          | 1.798147 |\n",
      "---------------------------------\n",
      "day: 2832, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 3582739.12\n",
      "total_reward: 2582739.12\n",
      "total_cost: 352797.13\n",
      "total_trades: 79419\n",
      "Sharpe: 0.745\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 111         |\n",
      "|    iterations           | 2           |\n",
      "|    time_elapsed         | 36          |\n",
      "|    total_timesteps      | 4096        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.019904409 |\n",
      "|    clip_fraction        | 0.273       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | -0.00292    |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 4.37        |\n",
      "|    n_updates            | 10          |\n",
      "|    policy_gradient_loss | -0.0277     |\n",
      "|    reward               | 0.3435778   |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 11.8        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 109         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 56          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.015447129 |\n",
      "|    clip_fraction        | 0.163       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | 0.00352     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 61.7        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0232     |\n",
      "|    reward               | -1.2225282  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 93          |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 109         |\n",
      "|    iterations           | 4           |\n",
      "|    time_elapsed         | 75          |\n",
      "|    total_timesteps      | 8192        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022528362 |\n",
      "|    clip_fraction        | 0.224       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | -0.00316    |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 34.4        |\n",
      "|    n_updates            | 30          |\n",
      "|    policy_gradient_loss | -0.021      |\n",
      "|    reward               | -0.2556716  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 53.1        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 108         |\n",
      "|    iterations           | 5           |\n",
      "|    time_elapsed         | 94          |\n",
      "|    total_timesteps      | 10240       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.021244759 |\n",
      "|    clip_fraction        | 0.23        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | -0.00305    |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 13.8        |\n",
      "|    n_updates            | 40          |\n",
      "|    policy_gradient_loss | -0.025      |\n",
      "|    reward               | -0.3963406  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 22.5        |\n",
      "-----------------------------------------\n",
      "======PPO Validation from:  2020-07-02 to  2020-10-01\n",
      "PPO Sharpe Ratio:  0.16589826865738153\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_189_1\n",
      "day: 2832, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4637158.62\n",
      "total_reward: 3637158.62\n",
      "total_cost: 1320.49\n",
      "total_trades: 28160\n",
      "Sharpe: 0.848\n",
      "=================================\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    episodes        | 4         |\n",
      "|    fps             | 72        |\n",
      "|    time_elapsed    | 155       |\n",
      "|    total_timesteps | 11332     |\n",
      "| train/             |           |\n",
      "|    actor_loss      | -86       |\n",
      "|    critic_loss     | 919       |\n",
      "|    learning_rate   | 0.0005    |\n",
      "|    n_updates       | 8499      |\n",
      "|    reward          | 2.9062877 |\n",
      "----------------------------------\n",
      "======DDPG Validation from:  2020-07-02 to  2020-10-01\n",
      "======Best Model Retraining from:  2009-04-01 to  2020-10-01\n",
      "======Trading from:  2020-10-01 to  2020-12-31\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2020-10-01\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_252_1\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 102         |\n",
      "|    iterations         | 100         |\n",
      "|    time_elapsed       | 4           |\n",
      "|    total_timesteps    | 500         |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 0.188       |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 99          |\n",
      "|    policy_loss        | -38.8       |\n",
      "|    reward             | -0.10735827 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 3.28        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 9         |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -0.381    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | 30.5      |\n",
      "|    reward             | 1.5049524 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 0.645     |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 300       |\n",
      "|    time_elapsed       | 14        |\n",
      "|    total_timesteps    | 1500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.0447   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 299       |\n",
      "|    policy_loss        | -126      |\n",
      "|    reward             | 3.5429835 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 15        |\n",
      "-------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 103         |\n",
      "|    iterations         | 400         |\n",
      "|    time_elapsed       | 19          |\n",
      "|    total_timesteps    | 2000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 399         |\n",
      "|    policy_loss        | -8.85       |\n",
      "|    reward             | -0.22506534 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 4.26        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 500       |\n",
      "|    time_elapsed       | 24        |\n",
      "|    total_timesteps    | 2500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 499       |\n",
      "|    policy_loss        | -184      |\n",
      "|    reward             | 0.7393677 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 48.6      |\n",
      "-------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 103         |\n",
      "|    iterations         | 600         |\n",
      "|    time_elapsed       | 28          |\n",
      "|    total_timesteps    | 3000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | -0.0462     |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 599         |\n",
      "|    policy_loss        | 94.5        |\n",
      "|    reward             | -0.30195987 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 5.89        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 700       |\n",
      "|    time_elapsed       | 33        |\n",
      "|    total_timesteps    | 3500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0.0312    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 699       |\n",
      "|    policy_loss        | 54.1      |\n",
      "|    reward             | 3.8608024 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 11        |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 800       |\n",
      "|    time_elapsed       | 38        |\n",
      "|    total_timesteps    | 4000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -0.661    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 799       |\n",
      "|    policy_loss        | -12.4     |\n",
      "|    reward             | 0.7427435 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 1.13      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 103      |\n",
      "|    iterations         | 900      |\n",
      "|    time_elapsed       | 43       |\n",
      "|    total_timesteps    | 4500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.3    |\n",
      "|    explained_variance | -0.261   |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 899      |\n",
      "|    policy_loss        | -200     |\n",
      "|    reward             | 0.750084 |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 28       |\n",
      "------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 103        |\n",
      "|    iterations         | 1000       |\n",
      "|    time_elapsed       | 48         |\n",
      "|    total_timesteps    | 5000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.0853     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 999        |\n",
      "|    policy_loss        | 165        |\n",
      "|    reward             | -2.6817133 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 17.5       |\n",
      "--------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 103      |\n",
      "|    iterations         | 1100     |\n",
      "|    time_elapsed       | 53       |\n",
      "|    total_timesteps    | 5500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.3    |\n",
      "|    explained_variance | 0        |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 1099     |\n",
      "|    policy_loss        | -113     |\n",
      "|    reward             | 8.745373 |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 9.76     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 1200      |\n",
      "|    time_elapsed       | 57        |\n",
      "|    total_timesteps    | 6000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -0.602    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1199      |\n",
      "|    policy_loss        | -86.4     |\n",
      "|    reward             | -1.147322 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 4.68      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1300       |\n",
      "|    time_elapsed       | 63         |\n",
      "|    total_timesteps    | 6500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.0163     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1299       |\n",
      "|    policy_loss        | -1.51      |\n",
      "|    reward             | -0.5397623 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.158      |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1400       |\n",
      "|    time_elapsed       | 68         |\n",
      "|    total_timesteps    | 7000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | -0.0211    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1399       |\n",
      "|    policy_loss        | -48.9      |\n",
      "|    reward             | -1.2171868 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 2.4        |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1500       |\n",
      "|    time_elapsed       | 72         |\n",
      "|    total_timesteps    | 7500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1499       |\n",
      "|    policy_loss        | -239       |\n",
      "|    reward             | -6.3630447 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 48.7       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1600       |\n",
      "|    time_elapsed       | 78         |\n",
      "|    total_timesteps    | 8000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 1.19e-07   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1599       |\n",
      "|    policy_loss        | -84        |\n",
      "|    reward             | -4.5349236 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 6.11       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1700       |\n",
      "|    time_elapsed       | 82         |\n",
      "|    total_timesteps    | 8500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 6.16e-05   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1699       |\n",
      "|    policy_loss        | 89         |\n",
      "|    reward             | -6.2646976 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 7.64       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1800       |\n",
      "|    time_elapsed       | 87         |\n",
      "|    total_timesteps    | 9000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | -0.1       |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1799       |\n",
      "|    policy_loss        | 18.7       |\n",
      "|    reward             | -1.2441337 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 1.57       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1900       |\n",
      "|    time_elapsed       | 92         |\n",
      "|    total_timesteps    | 9500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1899       |\n",
      "|    policy_loss        | 3.41       |\n",
      "|    reward             | -0.8480996 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.372      |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 102       |\n",
      "|    iterations         | 2000      |\n",
      "|    time_elapsed       | 97        |\n",
      "|    total_timesteps    | 10000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1999      |\n",
      "|    policy_loss        | 5.12      |\n",
      "|    reward             | 2.5999286 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 0.201     |\n",
      "-------------------------------------\n",
      "======A2C Validation from:  2020-10-01 to  2020-12-31\n",
      "A2C Sharpe Ratio:  0.2926392071140206\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_252_1\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    fps             | 114       |\n",
      "|    iterations      | 1         |\n",
      "|    time_elapsed    | 17        |\n",
      "|    total_timesteps | 2048      |\n",
      "| train/             |           |\n",
      "|    reward          | 2.1244433 |\n",
      "----------------------------------\n",
      "day: 2895, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4018680.90\n",
      "total_reward: 3018680.90\n",
      "total_cost: 367962.70\n",
      "total_trades: 81118\n",
      "Sharpe: 0.788\n",
      "=================================\n",
      "---------------------------------------\n",
      "| time/                   |           |\n",
      "|    fps                  | 110       |\n",
      "|    iterations           | 2         |\n",
      "|    time_elapsed         | 37        |\n",
      "|    total_timesteps      | 4096      |\n",
      "| train/                  |           |\n",
      "|    approx_kl            | 0.0224687 |\n",
      "|    clip_fraction        | 0.263     |\n",
      "|    clip_range           | 0.2       |\n",
      "|    entropy_loss         | -41.2     |\n",
      "|    explained_variance   | -0.0145   |\n",
      "|    learning_rate        | 0.00025   |\n",
      "|    loss                 | 3.74      |\n",
      "|    n_updates            | 10        |\n",
      "|    policy_gradient_loss | -0.0295   |\n",
      "|    reward               | 2.1768575 |\n",
      "|    std                  | 1         |\n",
      "|    value_loss           | 13.6      |\n",
      "---------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 109         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 56          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.019477358 |\n",
      "|    clip_fraction        | 0.214       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | -0.00418    |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 22.4        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0214     |\n",
      "|    reward               | -1.4381192  |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 68.7        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 108         |\n",
      "|    iterations           | 4           |\n",
      "|    time_elapsed         | 75          |\n",
      "|    total_timesteps      | 8192        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022186732 |\n",
      "|    clip_fraction        | 0.206       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | 0.00873     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 11.9        |\n",
      "|    n_updates            | 30          |\n",
      "|    policy_gradient_loss | -0.0247     |\n",
      "|    reward               | -5.417002   |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 40.3        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 107         |\n",
      "|    iterations           | 5           |\n",
      "|    time_elapsed         | 95          |\n",
      "|    total_timesteps      | 10240       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.020726388 |\n",
      "|    clip_fraction        | 0.25        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.4       |\n",
      "|    explained_variance   | -0.0907     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 7.05        |\n",
      "|    n_updates            | 40          |\n",
      "|    policy_gradient_loss | -0.0209     |\n",
      "|    reward               | -0.22937952 |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 12.4        |\n",
      "-----------------------------------------\n",
      "======PPO Validation from:  2020-10-01 to  2020-12-31\n",
      "PPO Sharpe Ratio:  0.3213694599777874\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_252_1\n",
      "day: 2895, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4359713.39\n",
      "total_reward: 3359713.39\n",
      "total_cost: 999.00\n",
      "total_trades: 28950\n",
      "Sharpe: 0.724\n",
      "=================================\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    episodes        | 4         |\n",
      "|    fps             | 72        |\n",
      "|    time_elapsed    | 159       |\n",
      "|    total_timesteps | 11584     |\n",
      "| train/             |           |\n",
      "|    actor_loss      | -86.7     |\n",
      "|    critic_loss     | 1.24e+03  |\n",
      "|    learning_rate   | 0.0005    |\n",
      "|    n_updates       | 8688      |\n",
      "|    reward          | 6.0803432 |\n",
      "----------------------------------\n",
      "======DDPG Validation from:  2020-10-01 to  2020-12-31\n",
      "======Best Model Retraining from:  2009-04-01 to  2020-12-31\n",
      "======Trading from:  2020-12-31 to  2021-04-05\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2020-12-31\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_315_1\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 102         |\n",
      "|    iterations         | 100         |\n",
      "|    time_elapsed       | 4           |\n",
      "|    total_timesteps    | 500         |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | -0.0771     |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 99          |\n",
      "|    policy_loss        | -47.5       |\n",
      "|    reward             | -0.36346516 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 4.48        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 103       |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 9         |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0.204     |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | 68.2      |\n",
      "|    reward             | 1.7017152 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 3.46      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 103      |\n",
      "|    iterations         | 300      |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 1500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.2    |\n",
      "|    explained_variance | 0.0208   |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 299      |\n",
      "|    policy_loss        | -261     |\n",
      "|    reward             | 3.446836 |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 42.6     |\n",
      "------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 400        |\n",
      "|    time_elapsed       | 19         |\n",
      "|    total_timesteps    | 2000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 3.52e-06   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 399        |\n",
      "|    policy_loss        | 17.9       |\n",
      "|    reward             | 0.10530679 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 1.99       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 102         |\n",
      "|    iterations         | 500         |\n",
      "|    time_elapsed       | 24          |\n",
      "|    total_timesteps    | 2500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | -0.211      |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 499         |\n",
      "|    policy_loss        | 208         |\n",
      "|    reward             | -0.90867454 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 25.7        |\n",
      "---------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 99       |\n",
      "|    iterations         | 600      |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 3000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.3    |\n",
      "|    explained_variance | 1.19e-07 |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 599      |\n",
      "|    policy_loss        | -19.9    |\n",
      "|    reward             | 0.811663 |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 0.476    |\n",
      "------------------------------------\n",
      "----------------------------------------\n",
      "| time/                 |              |\n",
      "|    fps                | 100          |\n",
      "|    iterations         | 700          |\n",
      "|    time_elapsed       | 34           |\n",
      "|    total_timesteps    | 3500         |\n",
      "| train/                |              |\n",
      "|    entropy_loss       | -41.3        |\n",
      "|    explained_variance | 0.0445       |\n",
      "|    learning_rate      | 0.0005       |\n",
      "|    n_updates          | 699          |\n",
      "|    policy_loss        | -94.3        |\n",
      "|    reward             | -0.116225086 |\n",
      "|    std                | 1.01         |\n",
      "|    value_loss         | 5.06         |\n",
      "----------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 800        |\n",
      "|    time_elapsed       | 39         |\n",
      "|    total_timesteps    | 4000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0.0921     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 799        |\n",
      "|    policy_loss        | 74.5       |\n",
      "|    reward             | 0.66946584 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 4.44       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 900         |\n",
      "|    time_elapsed       | 44          |\n",
      "|    total_timesteps    | 4500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.4       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 899         |\n",
      "|    policy_loss        | 85.6        |\n",
      "|    reward             | -0.23757298 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 5.8         |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 101       |\n",
      "|    iterations         | 1000      |\n",
      "|    time_elapsed       | 49        |\n",
      "|    total_timesteps    | 5000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -0.0811   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 999       |\n",
      "|    policy_loss        | 70.7      |\n",
      "|    reward             | 0.4755612 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 3.59      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 101       |\n",
      "|    iterations         | 1100      |\n",
      "|    time_elapsed       | 54        |\n",
      "|    total_timesteps    | 5500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | -0.0337   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1099      |\n",
      "|    policy_loss        | 106       |\n",
      "|    reward             | -3.194081 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 11.7      |\n",
      "-------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 101         |\n",
      "|    iterations         | 1200        |\n",
      "|    time_elapsed       | 59          |\n",
      "|    total_timesteps    | 6000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1199        |\n",
      "|    policy_loss        | 43.9        |\n",
      "|    reward             | -0.34790725 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 1.83        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 101       |\n",
      "|    iterations         | 1300      |\n",
      "|    time_elapsed       | 63        |\n",
      "|    total_timesteps    | 6500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | -0.0101   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1299      |\n",
      "|    policy_loss        | -36.8     |\n",
      "|    reward             | 1.0598992 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 1.37      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 101        |\n",
      "|    iterations         | 1400       |\n",
      "|    time_elapsed       | 68         |\n",
      "|    total_timesteps    | 7000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | -0.136     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1399       |\n",
      "|    policy_loss        | 147        |\n",
      "|    reward             | 0.57894623 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 15.1       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 101         |\n",
      "|    iterations         | 1500        |\n",
      "|    time_elapsed       | 73          |\n",
      "|    total_timesteps    | 7500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 0.247       |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1499        |\n",
      "|    policy_loss        | -123        |\n",
      "|    reward             | -0.42946026 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 8.91        |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1600       |\n",
      "|    time_elapsed       | 78         |\n",
      "|    total_timesteps    | 8000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 1.19e-07   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1599       |\n",
      "|    policy_loss        | 7.77       |\n",
      "|    reward             | -1.0654017 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.516      |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 102        |\n",
      "|    iterations         | 1700       |\n",
      "|    time_elapsed       | 83         |\n",
      "|    total_timesteps    | 8500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1699       |\n",
      "|    policy_loss        | 28.8       |\n",
      "|    reward             | -2.3461504 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 1.87       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 102         |\n",
      "|    iterations         | 1800        |\n",
      "|    time_elapsed       | 87          |\n",
      "|    total_timesteps    | 9000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | -1.89       |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1799        |\n",
      "|    policy_loss        | 58.2        |\n",
      "|    reward             | -0.80307704 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 4.2         |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 102       |\n",
      "|    iterations         | 1900      |\n",
      "|    time_elapsed       | 92        |\n",
      "|    total_timesteps    | 9500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | 0.0529    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1899      |\n",
      "|    policy_loss        | -77.3     |\n",
      "|    reward             | 1.3418723 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 11.7      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 102       |\n",
      "|    iterations         | 2000      |\n",
      "|    time_elapsed       | 97        |\n",
      "|    total_timesteps    | 10000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | -1.19e-07 |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1999      |\n",
      "|    policy_loss        | 126       |\n",
      "|    reward             | 1.904464  |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 10.8      |\n",
      "-------------------------------------\n",
      "======A2C Validation from:  2020-12-31 to  2021-04-05\n",
      "A2C Sharpe Ratio:  0.3175349505691884\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_315_1\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    fps             | 112       |\n",
      "|    iterations      | 1         |\n",
      "|    time_elapsed    | 18        |\n",
      "|    total_timesteps | 2048      |\n",
      "| train/             |           |\n",
      "|    reward          | 1.1830878 |\n",
      "----------------------------------\n",
      "day: 2958, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4024178.42\n",
      "total_reward: 3024178.42\n",
      "total_cost: 375817.39\n",
      "total_trades: 82840\n",
      "Sharpe: 0.776\n",
      "=================================\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 108        |\n",
      "|    iterations           | 2          |\n",
      "|    time_elapsed         | 37         |\n",
      "|    total_timesteps      | 4096       |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02013801 |\n",
      "|    clip_fraction        | 0.23       |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -41.1      |\n",
      "|    explained_variance   | -0.0231    |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 4.04       |\n",
      "|    n_updates            | 10         |\n",
      "|    policy_gradient_loss | -0.0282    |\n",
      "|    reward               | -1.6876341 |\n",
      "|    std                  | 1          |\n",
      "|    value_loss           | 11.2       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 106         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 57          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.016344294 |\n",
      "|    clip_fraction        | 0.187       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | 0.00876     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 21.4        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0226     |\n",
      "|    reward               | 0.88672215  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 61.5        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 106         |\n",
      "|    iterations           | 4           |\n",
      "|    time_elapsed         | 76          |\n",
      "|    total_timesteps      | 8192        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.021228999 |\n",
      "|    clip_fraction        | 0.226       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | 0.00595     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 24.8        |\n",
      "|    n_updates            | 30          |\n",
      "|    policy_gradient_loss | -0.023      |\n",
      "|    reward               | -2.0773363  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 67.6        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 106        |\n",
      "|    iterations           | 5          |\n",
      "|    time_elapsed         | 96         |\n",
      "|    total_timesteps      | 10240      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02048938 |\n",
      "|    clip_fraction        | 0.231      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -41.3      |\n",
      "|    explained_variance   | 0.013      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 8.01       |\n",
      "|    n_updates            | 40         |\n",
      "|    policy_gradient_loss | -0.018     |\n",
      "|    reward               | 0.60443103 |\n",
      "|    std                  | 1.01       |\n",
      "|    value_loss           | 18.2       |\n",
      "----------------------------------------\n",
      "======PPO Validation from:  2020-12-31 to  2021-04-05\n",
      "PPO Sharpe Ratio:  0.2847008485595188\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_315_1\n",
      "day: 2958, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4944737.56\n",
      "total_reward: 3944737.56\n",
      "total_cost: 1186.07\n",
      "total_trades: 57117\n",
      "Sharpe: 0.819\n",
      "=================================\n",
      "-----------------------------------\n",
      "| time/              |            |\n",
      "|    episodes        | 4          |\n",
      "|    fps             | 71         |\n",
      "|    time_elapsed    | 165        |\n",
      "|    total_timesteps | 11836      |\n",
      "| train/             |            |\n",
      "|    actor_loss      | 106        |\n",
      "|    critic_loss     | 2.32e+03   |\n",
      "|    learning_rate   | 0.0005     |\n",
      "|    n_updates       | 8877       |\n",
      "|    reward          | 0.61701614 |\n",
      "-----------------------------------\n",
      "======DDPG Validation from:  2020-12-31 to  2021-04-05\n",
      "======Best Model Retraining from:  2009-04-01 to  2021-04-05\n",
      "======Trading from:  2021-04-05 to  2021-07-02\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2021-04-05\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_378_1\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 99          |\n",
      "|    iterations         | 100         |\n",
      "|    time_elapsed       | 5           |\n",
      "|    total_timesteps    | 500         |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 99          |\n",
      "|    policy_loss        | -63.2       |\n",
      "|    reward             | -0.51650727 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 6.09        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 9         |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | -73.5     |\n",
      "|    reward             | 1.7030425 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 3.44      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 300       |\n",
      "|    time_elapsed       | 14        |\n",
      "|    total_timesteps    | 1500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0.076     |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 299       |\n",
      "|    policy_loss        | -218      |\n",
      "|    reward             | 3.3754418 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 31.9      |\n",
      "-------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 400         |\n",
      "|    time_elapsed       | 19          |\n",
      "|    total_timesteps    | 2000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 399         |\n",
      "|    policy_loss        | -29.7       |\n",
      "|    reward             | -0.14210798 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 1.87        |\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 500         |\n",
      "|    time_elapsed       | 24          |\n",
      "|    total_timesteps    | 2500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 1.01e-06    |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 499         |\n",
      "|    policy_loss        | 36.2        |\n",
      "|    reward             | -0.36307538 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 1.74        |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 600        |\n",
      "|    time_elapsed       | 29         |\n",
      "|    total_timesteps    | 3000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0.00194    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 599        |\n",
      "|    policy_loss        | -713       |\n",
      "|    reward             | -14.100862 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 470        |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 700        |\n",
      "|    time_elapsed       | 34         |\n",
      "|    total_timesteps    | 3500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 699        |\n",
      "|    policy_loss        | -27.5      |\n",
      "|    reward             | -1.5996127 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 0.576      |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 800         |\n",
      "|    time_elapsed       | 39          |\n",
      "|    total_timesteps    | 4000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 799         |\n",
      "|    policy_loss        | -25.5       |\n",
      "|    reward             | -0.86062217 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 0.577       |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 101       |\n",
      "|    iterations         | 900       |\n",
      "|    time_elapsed       | 44        |\n",
      "|    total_timesteps    | 4500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 899       |\n",
      "|    policy_loss        | 109       |\n",
      "|    reward             | 0.9339991 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 13.6      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1000      |\n",
      "|    time_elapsed       | 49        |\n",
      "|    total_timesteps    | 5000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 999       |\n",
      "|    policy_loss        | -44.5     |\n",
      "|    reward             | 0.8245403 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 2.41      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1100      |\n",
      "|    time_elapsed       | 54        |\n",
      "|    total_timesteps    | 5500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1099      |\n",
      "|    policy_loss        | -241      |\n",
      "|    reward             | 11.861711 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 43.3      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1200       |\n",
      "|    time_elapsed       | 59         |\n",
      "|    total_timesteps    | 6000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | -0.0974    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1199       |\n",
      "|    policy_loss        | 470        |\n",
      "|    reward             | -13.567417 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 165        |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1300      |\n",
      "|    time_elapsed       | 64        |\n",
      "|    total_timesteps    | 6500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0.00633   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1299      |\n",
      "|    policy_loss        | 22.4      |\n",
      "|    reward             | 1.3112776 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 0.483     |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1400      |\n",
      "|    time_elapsed       | 69        |\n",
      "|    total_timesteps    | 7000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1399      |\n",
      "|    policy_loss        | 21.2      |\n",
      "|    reward             | 1.9656771 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 2.53      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1500       |\n",
      "|    time_elapsed       | 74         |\n",
      "|    total_timesteps    | 7500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1499       |\n",
      "|    policy_loss        | -150       |\n",
      "|    reward             | -0.9432628 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 18.5       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1600      |\n",
      "|    time_elapsed       | 79        |\n",
      "|    total_timesteps    | 8000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1599      |\n",
      "|    policy_loss        | 6.74      |\n",
      "|    reward             | -2.869571 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 1.05      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1700       |\n",
      "|    time_elapsed       | 84         |\n",
      "|    total_timesteps    | 8500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1699       |\n",
      "|    policy_loss        | 13.1       |\n",
      "|    reward             | -1.4717982 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 39.1       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 1800        |\n",
      "|    time_elapsed       | 89          |\n",
      "|    total_timesteps    | 9000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 1.79e-07    |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1799        |\n",
      "|    policy_loss        | -108        |\n",
      "|    reward             | -0.08457257 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 13.2        |\n",
      "---------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1900       |\n",
      "|    time_elapsed       | 94         |\n",
      "|    total_timesteps    | 9500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.408      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1899       |\n",
      "|    policy_loss        | 83.8       |\n",
      "|    reward             | 0.28144893 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 4.43       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 2000        |\n",
      "|    time_elapsed       | 99          |\n",
      "|    total_timesteps    | 10000       |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.3       |\n",
      "|    explained_variance | 1.19e-07    |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 1999        |\n",
      "|    policy_loss        | 82.8        |\n",
      "|    reward             | -0.37743223 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 5.39        |\n",
      "---------------------------------------\n",
      "======A2C Validation from:  2021-04-05 to  2021-07-02\n",
      "A2C Sharpe Ratio:  0.07560430936486869\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_378_1\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    fps             | 110       |\n",
      "|    iterations      | 1         |\n",
      "|    time_elapsed    | 18        |\n",
      "|    total_timesteps | 2048      |\n",
      "| train/             |           |\n",
      "|    reward          | 0.9210871 |\n",
      "----------------------------------\n",
      "day: 3021, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 3955691.29\n",
      "total_reward: 2955691.29\n",
      "total_cost: 396604.41\n",
      "total_trades: 84498\n",
      "Sharpe: 0.795\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 106         |\n",
      "|    iterations           | 2           |\n",
      "|    time_elapsed         | 38          |\n",
      "|    total_timesteps      | 4096        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.018136818 |\n",
      "|    clip_fraction        | 0.26        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | -0.011      |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 4.89        |\n",
      "|    n_updates            | 10          |\n",
      "|    policy_gradient_loss | -0.0234     |\n",
      "|    reward               | 0.95122427  |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 8.7         |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 105         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 58          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.017552594 |\n",
      "|    clip_fraction        | 0.184       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | -0.000144   |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 7.87        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0212     |\n",
      "|    reward               | -0.08966285 |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 41.5        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 103         |\n",
      "|    iterations           | 4           |\n",
      "|    time_elapsed         | 78          |\n",
      "|    total_timesteps      | 8192        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.021466726 |\n",
      "|    clip_fraction        | 0.222       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | -0.00693    |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 42.2        |\n",
      "|    n_updates            | 30          |\n",
      "|    policy_gradient_loss | -0.0259     |\n",
      "|    reward               | 1.6671728   |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 60.6        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 103        |\n",
      "|    iterations           | 5          |\n",
      "|    time_elapsed         | 98         |\n",
      "|    total_timesteps      | 10240      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02369091 |\n",
      "|    clip_fraction        | 0.231      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -41.4      |\n",
      "|    explained_variance   | 0.0381     |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 3.66       |\n",
      "|    n_updates            | 40         |\n",
      "|    policy_gradient_loss | -0.0224    |\n",
      "|    reward               | 0.5051857  |\n",
      "|    std                  | 1.01       |\n",
      "|    value_loss           | 7.88       |\n",
      "----------------------------------------\n",
      "======PPO Validation from:  2021-04-05 to  2021-07-02\n",
      "PPO Sharpe Ratio:  0.11620566647210082\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_378_1\n",
      "day: 3021, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4467659.53\n",
      "total_reward: 3467659.53\n",
      "total_cost: 1432.15\n",
      "total_trades: 45425\n",
      "Sharpe: 0.738\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 4        |\n",
      "|    fps             | 70       |\n",
      "|    time_elapsed    | 171      |\n",
      "|    total_timesteps | 12088    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.68    |\n",
      "|    critic_loss     | 164      |\n",
      "|    learning_rate   | 0.0005   |\n",
      "|    n_updates       | 9066     |\n",
      "|    reward          | 5.309035 |\n",
      "---------------------------------\n",
      "======DDPG Validation from:  2021-04-05 to  2021-07-02\n",
      "======Best Model Retraining from:  2009-04-01 to  2021-07-02\n",
      "======Trading from:  2021-07-02 to  2021-10-01\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2021-07-02\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_441_1\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 98          |\n",
      "|    iterations         | 100         |\n",
      "|    time_elapsed       | 5           |\n",
      "|    total_timesteps    | 500         |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | -0.258      |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 99          |\n",
      "|    policy_loss        | -90.6       |\n",
      "|    reward             | -0.49389404 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 9.43        |\n",
      "---------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 99        |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 10        |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | -7.64     |\n",
      "|    reward             | 1.6369393 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 0.249     |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 300       |\n",
      "|    time_elapsed       | 14        |\n",
      "|    total_timesteps    | 1500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.235    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 299       |\n",
      "|    policy_loss        | -173      |\n",
      "|    reward             | 2.9572673 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 24.2      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 400        |\n",
      "|    time_elapsed       | 19         |\n",
      "|    total_timesteps    | 2000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | -0.0166    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 399        |\n",
      "|    policy_loss        | 12.1       |\n",
      "|    reward             | 0.07364806 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 0.723      |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 500        |\n",
      "|    time_elapsed       | 24         |\n",
      "|    total_timesteps    | 2500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 2.38e-07   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 499        |\n",
      "|    policy_loss        | -2.78      |\n",
      "|    reward             | 0.17163482 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 0.43       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 600        |\n",
      "|    time_elapsed       | 29         |\n",
      "|    total_timesteps    | 3000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | -0.016     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 599        |\n",
      "|    policy_loss        | -89.7      |\n",
      "|    reward             | -7.2966757 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 47.2       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 700        |\n",
      "|    time_elapsed       | 34         |\n",
      "|    total_timesteps    | 3500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | -1.41      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 699        |\n",
      "|    policy_loss        | -74.7      |\n",
      "|    reward             | -0.7702991 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 4.38       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 800       |\n",
      "|    time_elapsed       | 39        |\n",
      "|    total_timesteps    | 4000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 799       |\n",
      "|    policy_loss        | -85.5     |\n",
      "|    reward             | 0.7921747 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 7.76      |\n",
      "-------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 100         |\n",
      "|    iterations         | 900         |\n",
      "|    time_elapsed       | 44          |\n",
      "|    total_timesteps    | 4500        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.2       |\n",
      "|    explained_variance | 0.00927     |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 899         |\n",
      "|    policy_loss        | 249         |\n",
      "|    reward             | -0.18327725 |\n",
      "|    std                | 1           |\n",
      "|    value_loss         | 41.2        |\n",
      "---------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 100      |\n",
      "|    iterations         | 1000     |\n",
      "|    time_elapsed       | 49       |\n",
      "|    total_timesteps    | 5000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.2    |\n",
      "|    explained_variance | 0.0318   |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 999      |\n",
      "|    policy_loss        | -10.6    |\n",
      "|    reward             | 6.190973 |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 0.752    |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1100      |\n",
      "|    time_elapsed       | 54        |\n",
      "|    total_timesteps    | 5500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.0253   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1099      |\n",
      "|    policy_loss        | 91        |\n",
      "|    reward             | 3.7547379 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 16.4      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1200      |\n",
      "|    time_elapsed       | 59        |\n",
      "|    total_timesteps    | 6000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0.0386    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1199      |\n",
      "|    policy_loss        | 326       |\n",
      "|    reward             | -13.79838 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 83.7      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1300       |\n",
      "|    time_elapsed       | 64         |\n",
      "|    total_timesteps    | 6500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0.527      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1299       |\n",
      "|    policy_loss        | -57        |\n",
      "|    reward             | 0.40851548 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 2.24       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1400       |\n",
      "|    time_elapsed       | 69         |\n",
      "|    total_timesteps    | 7000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | -10.5      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1399       |\n",
      "|    policy_loss        | -40.1      |\n",
      "|    reward             | 0.72342026 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 1.42       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1500       |\n",
      "|    time_elapsed       | 74         |\n",
      "|    total_timesteps    | 7500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.367      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1499       |\n",
      "|    policy_loss        | -53.3      |\n",
      "|    reward             | 0.35161263 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 1.87       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 1600      |\n",
      "|    time_elapsed       | 79        |\n",
      "|    total_timesteps    | 8000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -0.0536   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1599      |\n",
      "|    policy_loss        | 139       |\n",
      "|    reward             | 2.9129677 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 29.9      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1700       |\n",
      "|    time_elapsed       | 84         |\n",
      "|    total_timesteps    | 8500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.00647    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1699       |\n",
      "|    policy_loss        | -148       |\n",
      "|    reward             | -2.1991248 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 21.2       |\n",
      "--------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 100      |\n",
      "|    iterations         | 1800     |\n",
      "|    time_elapsed       | 89       |\n",
      "|    total_timesteps    | 9000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.2    |\n",
      "|    explained_variance | 0        |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 1799     |\n",
      "|    policy_loss        | -320     |\n",
      "|    reward             | 5.558708 |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 71.4     |\n",
      "------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 100        |\n",
      "|    iterations         | 1900       |\n",
      "|    time_elapsed       | 94         |\n",
      "|    total_timesteps    | 9500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0.0264     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1899       |\n",
      "|    policy_loss        | 33.2       |\n",
      "|    reward             | 0.58614033 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.793      |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 100       |\n",
      "|    iterations         | 2000      |\n",
      "|    time_elapsed       | 99        |\n",
      "|    total_timesteps    | 10000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | 0.185     |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1999      |\n",
      "|    policy_loss        | -41.6     |\n",
      "|    reward             | 1.6578465 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 1.55      |\n",
      "-------------------------------------\n",
      "======A2C Validation from:  2021-07-02 to  2021-10-01\n",
      "A2C Sharpe Ratio:  -0.050955579763409344\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_441_1\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    fps             | 104       |\n",
      "|    iterations      | 1         |\n",
      "|    time_elapsed    | 19        |\n",
      "|    total_timesteps | 2048      |\n",
      "| train/             |           |\n",
      "|    reward          | 0.5096497 |\n",
      "----------------------------------\n",
      "day: 3084, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4017325.51\n",
      "total_reward: 3017325.51\n",
      "total_cost: 407890.85\n",
      "total_trades: 85899\n",
      "Sharpe: 0.786\n",
      "=================================\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 102        |\n",
      "|    iterations           | 2          |\n",
      "|    time_elapsed         | 39         |\n",
      "|    total_timesteps      | 4096       |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.01761775 |\n",
      "|    clip_fraction        | 0.224      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -41.2      |\n",
      "|    explained_variance   | -0.0273    |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 5.09       |\n",
      "|    n_updates            | 10         |\n",
      "|    policy_gradient_loss | -0.0287    |\n",
      "|    reward               | 0.64957666 |\n",
      "|    std                  | 1          |\n",
      "|    value_loss           | 10.8       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 102         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 60          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.020383295 |\n",
      "|    clip_fraction        | 0.207       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | -0.0251     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 15.6        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0221     |\n",
      "|    reward               | 0.8022679   |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 38.4        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 102         |\n",
      "|    iterations           | 4           |\n",
      "|    time_elapsed         | 80          |\n",
      "|    total_timesteps      | 8192        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.023377368 |\n",
      "|    clip_fraction        | 0.242       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.4       |\n",
      "|    explained_variance   | -0.0122     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 24.9        |\n",
      "|    n_updates            | 30          |\n",
      "|    policy_gradient_loss | -0.0224     |\n",
      "|    reward               | -0.73879576 |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 45.7        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 101         |\n",
      "|    iterations           | 5           |\n",
      "|    time_elapsed         | 100         |\n",
      "|    total_timesteps      | 10240       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.025654452 |\n",
      "|    clip_fraction        | 0.254       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.4       |\n",
      "|    explained_variance   | 0.00831     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 1.62        |\n",
      "|    n_updates            | 40          |\n",
      "|    policy_gradient_loss | -0.0292     |\n",
      "|    reward               | 0.39388952  |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 7.01        |\n",
      "-----------------------------------------\n",
      "======PPO Validation from:  2021-07-02 to  2021-10-01\n",
      "PPO Sharpe Ratio:  -0.24769608029914061\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_441_1\n",
      "day: 3084, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 5958205.37\n",
      "total_reward: 4958205.37\n",
      "total_cost: 999.00\n",
      "total_trades: 46211\n",
      "Sharpe: 0.861\n",
      "=================================\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    episodes        | 4         |\n",
      "|    fps             | 69        |\n",
      "|    time_elapsed    | 176       |\n",
      "|    total_timesteps | 12340     |\n",
      "| train/             |           |\n",
      "|    actor_loss      | 25        |\n",
      "|    critic_loss     | 138       |\n",
      "|    learning_rate   | 0.0005    |\n",
      "|    n_updates       | 9255      |\n",
      "|    reward          | 2.8471727 |\n",
      "----------------------------------\n",
      "======DDPG Validation from:  2021-07-02 to  2021-10-01\n",
      "======Best Model Retraining from:  2009-04-01 to  2021-10-01\n",
      "======Trading from:  2021-10-01 to  2021-12-31\n",
      "============================================\n",
      "turbulence_threshold:  203.40302023677697\n",
      "======Model training from:  2009-04-01 to  2021-10-01\n",
      "======A2C Training========\n",
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/a2c/a2c_504_1\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 97        |\n",
      "|    iterations         | 100       |\n",
      "|    time_elapsed       | 5         |\n",
      "|    total_timesteps    | 500       |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.3     |\n",
      "|    explained_variance | -0.00463  |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 99        |\n",
      "|    policy_loss        | -71.6     |\n",
      "|    reward             | 1.4474013 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 13.7      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 98        |\n",
      "|    iterations         | 200       |\n",
      "|    time_elapsed       | 10        |\n",
      "|    total_timesteps    | 1000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | -0.158    |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 199       |\n",
      "|    policy_loss        | 61.8      |\n",
      "|    reward             | 0.9088276 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 3.85      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 98        |\n",
      "|    iterations         | 300       |\n",
      "|    time_elapsed       | 15        |\n",
      "|    total_timesteps    | 1500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.4     |\n",
      "|    explained_variance | 0.412     |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 299       |\n",
      "|    policy_loss        | -172      |\n",
      "|    reward             | 3.5694954 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 23.8      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 98         |\n",
      "|    iterations         | 400        |\n",
      "|    time_elapsed       | 20         |\n",
      "|    total_timesteps    | 2000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0.216      |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 399        |\n",
      "|    policy_loss        | 0.284      |\n",
      "|    reward             | 0.84193903 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 1.06       |\n",
      "--------------------------------------\n",
      "----------------------------------------\n",
      "| time/                 |              |\n",
      "|    fps                | 98           |\n",
      "|    iterations         | 500          |\n",
      "|    time_elapsed       | 25           |\n",
      "|    total_timesteps    | 2500         |\n",
      "| train/                |              |\n",
      "|    entropy_loss       | -41.4        |\n",
      "|    explained_variance | 0            |\n",
      "|    learning_rate      | 0.0005       |\n",
      "|    n_updates          | 499          |\n",
      "|    policy_loss        | -11.4        |\n",
      "|    reward             | -0.003463573 |\n",
      "|    std                | 1.01         |\n",
      "|    value_loss         | 3.7          |\n",
      "----------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 98         |\n",
      "|    iterations         | 600        |\n",
      "|    time_elapsed       | 30         |\n",
      "|    total_timesteps    | 3000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 599        |\n",
      "|    policy_loss        | 492        |\n",
      "|    reward             | -1.0832251 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 170        |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 98         |\n",
      "|    iterations         | 700        |\n",
      "|    time_elapsed       | 35         |\n",
      "|    total_timesteps    | 3500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.4      |\n",
      "|    explained_variance | -0.0779    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 699        |\n",
      "|    policy_loss        | 5.71       |\n",
      "|    reward             | -1.1875958 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 1.95       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 98        |\n",
      "|    iterations         | 800       |\n",
      "|    time_elapsed       | 40        |\n",
      "|    total_timesteps    | 4000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.5     |\n",
      "|    explained_variance | 0.00461   |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 799       |\n",
      "|    policy_loss        | 31.7      |\n",
      "|    reward             | 0.8785168 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 2.08      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 98         |\n",
      "|    iterations         | 900        |\n",
      "|    time_elapsed       | 45         |\n",
      "|    total_timesteps    | 4500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | -0.0122    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 899        |\n",
      "|    policy_loss        | -143       |\n",
      "|    reward             | 0.91995525 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 12.7       |\n",
      "--------------------------------------\n",
      "---------------------------------------\n",
      "| time/                 |             |\n",
      "|    fps                | 98          |\n",
      "|    iterations         | 1000        |\n",
      "|    time_elapsed       | 50          |\n",
      "|    total_timesteps    | 5000        |\n",
      "| train/                |             |\n",
      "|    entropy_loss       | -41.5       |\n",
      "|    explained_variance | 0           |\n",
      "|    learning_rate      | 0.0005      |\n",
      "|    n_updates          | 999         |\n",
      "|    policy_loss        | 20.1        |\n",
      "|    reward             | -0.13694042 |\n",
      "|    std                | 1.01        |\n",
      "|    value_loss         | 1.76        |\n",
      "---------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 98       |\n",
      "|    iterations         | 1100     |\n",
      "|    time_elapsed       | 55       |\n",
      "|    total_timesteps    | 5500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -41.5    |\n",
      "|    explained_variance | 0        |\n",
      "|    learning_rate      | 0.0005   |\n",
      "|    n_updates          | 1099     |\n",
      "|    policy_loss        | 192      |\n",
      "|    reward             | 2.317354 |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 30       |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 98        |\n",
      "|    iterations         | 1200      |\n",
      "|    time_elapsed       | 60        |\n",
      "|    total_timesteps    | 6000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.5     |\n",
      "|    explained_variance | 1.19e-07  |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1199      |\n",
      "|    policy_loss        | -114      |\n",
      "|    reward             | 0.3874875 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 10.8      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 1300       |\n",
      "|    time_elapsed       | 66         |\n",
      "|    total_timesteps    | 6500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | -0.683     |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1299       |\n",
      "|    policy_loss        | 13.5       |\n",
      "|    reward             | -1.7721303 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.609      |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 97        |\n",
      "|    iterations         | 1400      |\n",
      "|    time_elapsed       | 71        |\n",
      "|    total_timesteps    | 7000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.5     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1399      |\n",
      "|    policy_loss        | -1.97     |\n",
      "|    reward             | 1.2080222 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 0.0575    |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 1500       |\n",
      "|    time_elapsed       | 76         |\n",
      "|    total_timesteps    | 7500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1499       |\n",
      "|    policy_loss        | -73.7      |\n",
      "|    reward             | -2.5547423 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 3.4        |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 1600       |\n",
      "|    time_elapsed       | 81         |\n",
      "|    total_timesteps    | 8000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1599       |\n",
      "|    policy_loss        | 43.5       |\n",
      "|    reward             | -7.6830072 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 5.33       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 97        |\n",
      "|    iterations         | 1700      |\n",
      "|    time_elapsed       | 86        |\n",
      "|    total_timesteps    | 8500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.5     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0005    |\n",
      "|    n_updates          | 1699      |\n",
      "|    policy_loss        | -85.5     |\n",
      "|    reward             | 2.9912035 |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 8.69      |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 1800       |\n",
      "|    time_elapsed       | 92         |\n",
      "|    total_timesteps    | 9000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | 0.00678    |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1799       |\n",
      "|    policy_loss        | 7.67       |\n",
      "|    reward             | 0.92055494 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 129        |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 1900       |\n",
      "|    time_elapsed       | 97         |\n",
      "|    total_timesteps    | 9500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | 1.19e-07   |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1899       |\n",
      "|    policy_loss        | -35.4      |\n",
      "|    reward             | -0.8627294 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 0.801      |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 97         |\n",
      "|    iterations         | 2000       |\n",
      "|    time_elapsed       | 102        |\n",
      "|    total_timesteps    | 10000      |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.5      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0005     |\n",
      "|    n_updates          | 1999       |\n",
      "|    policy_loss        | -93.2      |\n",
      "|    reward             | -1.4607706 |\n",
      "|    std                | 1.01       |\n",
      "|    value_loss         | 6.85       |\n",
      "--------------------------------------\n",
      "======A2C Validation from:  2021-10-01 to  2021-12-31\n",
      "A2C Sharpe Ratio:  0.14826048345115309\n",
      "======PPO Training========\n",
      "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ppo/ppo_504_1\n",
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    fps             | 106       |\n",
      "|    iterations      | 1         |\n",
      "|    time_elapsed    | 19        |\n",
      "|    total_timesteps | 2048      |\n",
      "| train/             |           |\n",
      "|    reward          | 1.1232015 |\n",
      "----------------------------------\n",
      "day: 3147, episode: 5\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 4607673.70\n",
      "total_reward: 3607673.70\n",
      "total_cost: 429135.74\n",
      "total_trades: 87943\n",
      "Sharpe: 0.840\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 103         |\n",
      "|    iterations           | 2           |\n",
      "|    time_elapsed         | 39          |\n",
      "|    total_timesteps      | 4096        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.017987348 |\n",
      "|    clip_fraction        | 0.245       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.2       |\n",
      "|    explained_variance   | -0.000848   |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 6.37        |\n",
      "|    n_updates            | 10          |\n",
      "|    policy_gradient_loss | -0.0251     |\n",
      "|    reward               | -0.17164417 |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 17.4        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 102         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 60          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.014718052 |\n",
      "|    clip_fraction        | 0.21        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.3       |\n",
      "|    explained_variance   | 0.00849     |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 29.6        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0212     |\n",
      "|    reward               | -5.57334    |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 64.5        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 101        |\n",
      "|    iterations           | 4          |\n",
      "|    time_elapsed         | 80         |\n",
      "|    total_timesteps      | 8192       |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.01915358 |\n",
      "|    clip_fraction        | 0.219      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -41.4      |\n",
      "|    explained_variance   | -0.0111    |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 23.9       |\n",
      "|    n_updates            | 30         |\n",
      "|    policy_gradient_loss | -0.023     |\n",
      "|    reward               | -2.2945697 |\n",
      "|    std                  | 1.01       |\n",
      "|    value_loss           | 40.7       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 101         |\n",
      "|    iterations           | 5           |\n",
      "|    time_elapsed         | 101         |\n",
      "|    total_timesteps      | 10240       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.026784968 |\n",
      "|    clip_fraction        | 0.284       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -41.5       |\n",
      "|    explained_variance   | -0.000641   |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 4.7         |\n",
      "|    n_updates            | 40          |\n",
      "|    policy_gradient_loss | -0.0265     |\n",
      "|    reward               | 1.150916    |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 12.6        |\n",
      "-----------------------------------------\n",
      "======PPO Validation from:  2021-10-01 to  2021-12-31\n",
      "PPO Sharpe Ratio:  0.11419680551558446\n",
      "======DDPG Training========\n",
      "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
      "Using cuda device\n",
      "Logging to tensorboard_log/ddpg/ddpg_504_1\n",
      "day: 3147, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 7592159.18\n",
      "total_reward: 6592159.18\n",
      "total_cost: 1068.22\n",
      "total_trades: 40943\n",
      "Sharpe: 0.910\n",
      "=================================\n",
      "-----------------------------------\n",
      "| time/              |            |\n",
      "|    episodes        | 4          |\n",
      "|    fps             | 69         |\n",
      "|    time_elapsed    | 182        |\n",
      "|    total_timesteps | 12592      |\n",
      "| train/             |            |\n",
      "|    actor_loss      | 16.6       |\n",
      "|    critic_loss     | 559        |\n",
      "|    learning_rate   | 0.0005     |\n",
      "|    n_updates       | 9444       |\n",
      "|    reward          | -15.986502 |\n",
      "-----------------------------------\n",
      "======DDPG Validation from:  2021-10-01 to  2021-12-31\n",
      "======Best Model Retraining from:  2009-04-01 to  2021-12-31\n",
      "======Trading from:  2021-12-31 to  2022-04-01\n",
      "Ensemble Strategy took:  45.364221183458966  minutes\n"
     ]
    }
   ],
   "source": [
    "df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,\n",
    "                                                 PPO_model_kwargs,\n",
    "                                                 DDPG_model_kwargs,\n",
    "                                                 timesteps_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "-0qd8acMtj1f",
    "outputId": "a7cc6220-7c14-4b18-964d-c79c1aca731d"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "  <div id=\"df-5f41faa3-d3f8-4c54-8223-23691754f392\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Iter</th>\n",
       "      <th>Val Start</th>\n",
       "      <th>Val End</th>\n",
       "      <th>Model Used</th>\n",
       "      <th>A2C Sharpe</th>\n",
       "      <th>PPO Sharpe</th>\n",
       "      <th>DDPG Sharpe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>126</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>A2C</td>\n",
       "      <td>0.222939</td>\n",
       "      <td>0.156513</td>\n",
       "      <td>0.205619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>189</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>0.115658</td>\n",
       "      <td>0.165898</td>\n",
       "      <td>0.270485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>PPO</td>\n",
       "      <td>0.292639</td>\n",
       "      <td>0.321369</td>\n",
       "      <td>0.227105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>315</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>0.317535</td>\n",
       "      <td>0.284701</td>\n",
       "      <td>0.395257</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>378</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>0.075604</td>\n",
       "      <td>0.116206</td>\n",
       "      <td>0.235294</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>441</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>A2C</td>\n",
       "      <td>-0.050956</td>\n",
       "      <td>-0.247696</td>\n",
       "      <td>-0.176675</td>\n",
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       "      <th>6</th>\n",
       "      <td>504</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>0.14826</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>0.249511</td>\n",
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      ],
      "text/plain": [
       "  Iter   Val Start     Val End Model Used A2C Sharpe PPO Sharpe DDPG Sharpe\n",
       "0  126  2020-04-02  2020-07-02        A2C   0.222939   0.156513    0.205619\n",
       "1  189  2020-07-02  2020-10-01       DDPG   0.115658   0.165898    0.270485\n",
       "2  252  2020-10-01  2020-12-31        PPO   0.292639   0.321369    0.227105\n",
       "3  315  2020-12-31  2021-04-05       DDPG   0.317535   0.284701    0.395257\n",
       "4  378  2021-04-05  2021-07-02       DDPG   0.075604   0.116206    0.235294\n",
       "5  441  2021-07-02  2021-10-01        A2C  -0.050956  -0.247696   -0.176675\n",
       "6  504  2021-10-01  2021-12-31       DDPG    0.14826   0.114197    0.249511"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6vvNSC6h1jZ"
   },
   "source": [
    "<a id='6'></a>\n",
    "# Part 7: Backtest Our Strategy\n",
    "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "X4JKB--8tj1g"
   },
   "outputs": [],
   "source": [
    "unique_trade_date = processed[(processed.date > val_test_start)&(processed.date <= val_test_end)].date.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q9mKF7GGtj1g",
    "outputId": "747f2942-010b-4df8-c66f-5cc2a583e704",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe Ratio:  1.2861291895889375\n"
     ]
    }
   ],
   "source": [
    "df_trade_date = pd.DataFrame({'datadate':unique_trade_date})\n",
    "\n",
    "df_account_value=pd.DataFrame()\n",
    "for i in range(rebalance_window+validation_window, len(unique_trade_date)+1,rebalance_window):\n",
    "    temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format('ensemble',i))\n",
    "    df_account_value = df_account_value.append(temp,ignore_index=True)\n",
    "sharpe=(252**0.5)*df_account_value.account_value.pct_change(1).mean()/df_account_value.account_value.pct_change(1).std()\n",
    "print('Sharpe Ratio: ',sharpe)\n",
    "df_account_value=df_account_value.join(df_trade_date[validation_window:].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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     "height": 206
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    "id": "oyosyW7_tj1g",
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    {
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       "      <th>account_value</th>\n",
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       "   account_value        date  daily_return    datadate\n",
       "0   1.000000e+06  2020-07-02           NaN  2020-07-02\n",
       "1   1.002711e+06  2020-07-06      0.002711  2020-07-06\n",
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       "4   9.937092e+05  2020-07-09     -0.006596  2020-07-09"
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df_account_value.account_value.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lr2zX7ZxNyFQ"
   },
   "source": [
    "<a id='6.1'></a>\n",
    "## 7.1 BackTestStats\n",
    "pass in df_account_value, this information is stored in env class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nzkr9yv-AdV_",
    "outputId": "8bc18db6-17cc-4763-f3a5-01083861401d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Backtest Results===========\n",
      "Annual return          0.208496\n",
      "Cumulative returns     0.392929\n",
      "Annual volatility      0.157206\n",
      "Sharpe ratio           1.286129\n",
      "Calmar ratio           1.406483\n",
      "Stability              0.854001\n",
      "Max drawdown          -0.148239\n",
      "Omega ratio            1.250757\n",
      "Sortino ratio          1.939285\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.046083\n",
      "Daily value at risk   -0.019004\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"==============Get Backtest Results===========\")\n",
    "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
    "\n",
    "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
    "perf_stats_all = pd.DataFrame(perf_stats_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DiHhM1YkoCel",
    "outputId": "63d7e627-d1d2-42b5-9038-17aa4ce853cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Baseline Stats===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (440, 8)\n",
      "Annual return          0.194576\n",
      "Cumulative returns     0.364011\n",
      "Annual volatility      0.143979\n",
      "Sharpe ratio           1.309991\n",
      "Calmar ratio           1.718341\n",
      "Stability              0.794668\n",
      "Max drawdown          -0.113235\n",
      "Omega ratio            1.241402\n",
      "Sortino ratio          1.912275\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.061251\n",
      "Daily value at risk   -0.017391\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#baseline stats\n",
    "print(\"==============Get Baseline Stats===========\")\n",
    "baseline_df = get_baseline(\n",
    "        ticker=\"^DJI\", \n",
    "        start = df_account_value.loc[0,'date'],\n",
    "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
    "\n",
    "stats = backtest_stats(baseline_df, value_col_name = 'close')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6Suru3h1jc"
   },
   "source": [
    "<a id='6.2'></a>\n",
    "## 7.2 BackTestPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "HggausPRoCem",
    "outputId": "5e1cc98c-2654-482c-e0ef-84bc74d43443"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Compare to DJIA===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (440, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-02</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2022-03-31</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>21</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>20.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>39.293%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>15.721%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-14.824%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-1.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.82</td>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>2022-03-08</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.25</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.60</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>2020-09-23</td>\n",
       "      <td>2020-10-07</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.61</td>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.37</td>\n",
       "      <td>2021-05-10</td>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stress Events</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New Normal</th>\n",
       "      <td>0.08%</td>\n",
       "      <td>-3.54%</td>\n",
       "      <td>5.14%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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",
      "text/plain": [
       "<Figure size 1008x5184 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"==============Compare to DJIA===========\")\n",
    "%matplotlib inline\n",
    "# S&P 500: ^GSPC\n",
    "# Dow Jones Index: ^DJI\n",
    "# NASDAQ 100: ^NDX\n",
    "backtest_plot(df_account_value, \n",
    "             baseline_ticker = '^DJI', \n",
    "             baseline_start = df_account_value.loc[0,'date'],\n",
    "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "MkdWMUWKoCem"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
